基于随机模型预测控制的智能网联车与人驾车辆混合队列协同控制理论研究

Due to the advanced capabilities in improving traffic safety and efficiency, the platoon control of the connected and autonomous vehicle (CAV) system has obtained extensive research interests. However, there will be a transition period where we will have a mixed flow of CAV and human-drive vehicle (HDV) since the population of HDV is enormous all over the world. The existing studies mainly focus on a pure CAV flow, which ignores the properties of the mixed flow and the car-following (CF) behaviors of the HDV. The advantage of platoon control focusing on pure CAV flow may be impaired in a mixed flow context. Motivated by the above research gap, this study proposes a cooperative platoon control strategy, especially for a platoon mixed with CAVs and HDVs, which tries to improve the efficiency of the mixed traffic flow. The proposed research will first analyze the evolution of human drivers’ CF behaviors and its uncertainty. A short-term anticipation algorithm will be established to forecast the CF behaviors and uncertainty of HDVs in a mixed platoon. Built upon that, a constrained stochastic model productive control (SMPC) approach is developed to control the movement of the CAVs in the platoon so that we can ensure both transient traffic smoothness and asymptotic stability of this mixed platoon. To fully utilize the cooperative computation capability of CAV system, an online distributed algorithm is proposed to solve the SMPC and implement the real-time control. The control parameters will be determined by the multi-level stability analysis, which evolves asymptotic stability, string stability, and traffic flow stability. The proposed study will be demonstrated by the field test in the CAV testbed of Chang’an University. The outcomes of this project will potentially contribute to both the literature and practice in managing the mixed traffic flow as well as the designing of CAV platoon control.

智能网联车队列控制技术在提升交通流效率及安全性上拥有巨大潜力，获得业界广泛关注。而传统车辆保有量巨大，必将和智能网联车长期共存，形成混合交通流。现有面向纯智能网联车队列的控制策略，并未考虑混合流特性及人驾车辆跟驰行为，存在一定的局限性。基于上述问题，本课题拟构建面向智能网联车与人驾车辆混合队列的协同控制理论，提升混合交通流效率。本研究将首先探索混合流环境下，人驾车辆跟驰行为及其不确定性的变化，建立预测模型；通过综合人驾车辆跟驰行为、控制及交通流效率等多种因素，基于随机模型预测控制，构建协同控制模型；为实现协同控制模型的高效求解和实时控制，拟建立分布式算法，充分利用智能网联车的协同计算能力；根据对系统及交通流多重稳定性的理论分析，确定控制参数可行域；最后拟通过协同控制理论实车运用和场地测试对项目成果进行验证。本研究将为提升混合交通流下道路交通系统的效率和安全性，提供重要的理论指导和实证支持。

智能网联车队列控制技术在提升交通流效率及安全性上拥有巨大潜力，获得业界广泛关注。而传统车辆保有量巨大，必将和智能网联车长期共存，形成混合交通流。现有面向纯智能网联车队列的控制策略，并未考虑混合流特性及人驾车辆跟驰行为，存在一定的局限性。基于上述问题，本课题拟构建面向智能网联车与人驾车辆混合队列的协同控制理论，提升混合交通流效率。本研究将首先探索混合流环境下，人驾车辆跟驰行为及其不确定性的变化，建立预测模型；通过综合人驾车辆跟驰行为、控制及交通流效率等多种因素，基于随机模型预测控制，构建协同控制模型；为实现协同控制模型的高效求解和实时控制，拟建立分布式算法，充分利用智能网联车的协同计算能力；根据对系统及交通流多重稳定性的理论分析，确定控制参数可行域；最后，拟通过协同控制理论实车运用和场地测试对项目成果进行验证。本研究将为提升混合交通流下道路交通系统的效率和安全性，提供重要的理论指导和实证支持。